@inproceedings{6d21efed7b084cfa9a6ae08f31458b50,

title = "Ranking with large margin principle: Two approaches",

abstract = "We discuss the problem of ranking k instances with the use of a {"}large margin{"} principle. We introduce two main approaches: the first is the {"}fixed margin{"} policy in which the margin of the closest neighboring classes is being maximized - which turns out to be a direct generalization of SVM to ranking learning. The second approach allows for k - 1 different margins where the sum of margins is maximized. This approach is shown to reduce to v-SVM when the number of classes k = 2. Both approaches are optimal in size of 21 where l is the total number of training examples. Experiments performed on visual classification and {"}collaborative filtering{"} show that both approaches outperform existing ordinal regression algorithms applied for ranking and multi-class SVM applied to general multi-class classification.",

author = "Amnon Shashua and Anat Levin",

year = "2003",

language = "American English",

isbn = "0262025507",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002",

note = "16th Annual Neural Information Processing Systems Conference, NIPS 2002 ; Conference date: 09-12-2002 Through 14-12-2002",

}